Overview of observational data-based time series causal inference

With the increase of data storage and the improvement of computing power,using observational data to infer time series causality has become a novel approach.Based on the properties and research status of time series causal inference,five observational data-based methods were induced,including Grange...

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Bibliographic Details
Published in大数据 Vol. 9; pp. 139 - 158
Main Authors Zefan ZENG, Siya CHEN, Xi LONG, Guang JIN
Format Journal Article
LanguageChinese
Published China InfoCom Media Group 01.07.2023
Subjects
time series;causal inference;Granger causal analysis;information entropy;Bayesian network;structural causal model;nonlinear state space model
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Summary:With the increase of data storage and the improvement of computing power,using observational data to infer time series causality has become a novel approach.Based on the properties and research status of time series causal inference,five observational data-based methods were induced,including Granger causal analysis,information theory-based method,causal network structure learning algorithm,structural causal model-based method and method based on nonlinear state-space model.Then we briefly introduced typical applications in economics and finance,medical science and biology,earth system science and other engineering fields.Further,we compared the advantages and disadvantages and analyzed the ways for improvement of the five methods according to the focus and difficulties of time series causal inference.Finally,we looked into the future research directions.
ISSN:2096-0271